Search

Abstract Freshwater ecosystems are biologically important habitats that provide many ecosystem services. Calcium concentration and pH are two key variables that are linked to multiple chemical processes in these environments, influence the biology of organisms from diverse taxa, and can be important factors affecting the distribution of native and non-native species. However, it can be challenging to obtain high-resolution data for these variables at regional and national scales. To address this data gap, water quality data for lakes and rivers in Canada and the continental USA were compiled and used to generate high-resolution (10 × 10 km) interpolated raster layers, after comparing multiple spatial interpolation approaches. This is the first time that such data have been made available at this scale and resolution, providing a valuable resource for research, including projects evaluating risks from environmental change, pollution, and invasive species. This will aid the development of conservation and management strategies for these vital habitats.

Abstract Habitat structural complexity (HSC) is a key variable to help interpret ecological patterns and processes among different ecosystems. Existing metrics used to quantify HSC often, however, result in insufficient or biased representation of structural complexity. As such, our understanding of how HSC affects biodiversity and related ecological patterns is often limited by these measures. Recent advances in photogrammetry and computer vision have enabled the ability to reconstruct 3D habitats with high efficiency and accuracy. Point clouds, for example, better represent the structural properties of target objects with reduced sampling bias as compared to traditional formats like 2.5D raster (i.e. DEM, DSM, DTM). The analysis of point clouds is, however, limited by the lack of readily available packages with mathematically well‐defined metrics for ecologists to help interpret relevant HSC properties. To address this gap, novel metrics are provided in the present Python package HSC3D version 0.2.0, which allows quantification of structural complexity of targeted habitats based on photogrammetry point clouds. This package is designed to help ecologists better describe the structural characteristics of a specific habitat and is bundled with visualisation functions to help interpret the computational processes used. To demonstrate functions implemented in HSC3D we use a case study that compares the structural complexity differences formed by two intertidal ecosystem engineers, mussels and oysters. Results indicate that HSC3D is a versatile and easily adapted package, which can provide both ecological and analytical insights on photogrammetry point clouds which should prove a useful tool for ecologists wanting to quantify HSC.

In recent years, the complex evolution of the highly pathogenic avian influenza (HPAI) situation reflects a change in the eco-epidemiology of the causative agent which, among other demands, renews the need for better understanding of the connectivity between countries through the main virus reservoirs to improve prevention, early warning and mitigate the associated risks. Our objective was to determine migratory connectivity of Blue-winged Teal and evaluate the risk of AIV introduction to Cuba by this species. The stable hydrogen isotope (δ2H) value in flight feathers was analyzed. Individuals were sampled during the migratory season of 2021 (N=126) and winter residence of 2020 (N=152), in western and central Cuba, respectively. Based on banding records from 1955 to 2018, the transition probabilities from 3 breeding areas in North America to 9 wintering areas was estimated with a Burnham’s live-recapture dead-recovery modeling framework. A map of likely origin in North America of the individuals sampled for each season was generated, combining the isotopic information and transition probabilities. Evidence of an age-related and phenological pattern in migratory origins was identified. Individuals harvested in the migratory season in western Cuba were most likely from molt or natal areas in the prairies and forest regions from United States and southern Canada. Alternatively, individuals harvested in winter in the center of Cuba had the most likely origin in the U.S. prairie region. The spatial-temporal pattern of AIV prevalence in the estimated region was analyzed and suggestions are made to assist the Cuban system of active AIV surveillance of wild birds.

The Society of Nuclear Medicine and Molecular Imaging (SNMMI) has publicly commented that they do not support the reporting of large extravasations to patients or regulatory bodies. The comment cites recently published articles suggesting that extravasations are infrequent and not severe. The comment stresses the importance of ensuring patients are not apprehensive or resistant to nuclear medicine procedures because of “radiation paranoia” and a “chilling effect” that can result from misinformation. Radiation paranoia and chilling effect are not defined, and there are no references to specific misinformation. Our experiences and this case suggest the comment may be incongruent with real-world clinical experiences. Our severe case, at a center with a long-standing focus on reducing radiopharmaceutical extravasation, suggests these events can still happen, can be significant, and should be shared with our patients. Our experiences also suggest that being transparent with patients builds trust. We are concerned that a reluctance to recognize the true frequency of extravasations and their severity may create distrust in the relationship between the nuclear medicine community and patients.

Background: Studies are increasingly examining the relationship between the neighborhood environment and cognitive decline; yet, few have investigated associations between multiple neighborhood features and Alzheimer's disease and related dementias (ADRD). Objective: We investigated the relationship between neighborhood features and ADRD cumulative incidence from 2010 to 2014 in the South Carolina Alzheimer's Disease Registry (SCADR). Methods: Diagnosed ADRD cases [greater than or equal to] 50 years of age were ascertained from the SCADR by ZIP code and census tract. Neighborhood features from multiple secondary sources included poverty, air pollution [particulate matter with a diameter of 2.5 micrometers or less (P[M.sub.2.5])], and rurality at the census-tract level and access to healthy food, recreation facilities, and diabetes screening at the county level. In addition to using Poisson generalized linear regression to estimate ADRD incident rate ratios (IRR) with 95% confidence intervals (CIs), we applied integrated nested Laplace approximations and stochastic partial differential equations (INLA-SPDE) to address disparate spatial scales. We estimated associations between neighborhood features and ADRD cumulative incidence. Results: The average annual ADRD cumulative incidence was 690 per 100,000 people per census tract (95% CI: 660, 710). The analysis was limited to 98% of census tracts with a population [greater than or equal to]50 years old (i.e., 1,081 of 1,103). The average percent of families living below the federal poverty line per census tract was 18.8%, and ~20% of census tracts were considered rural. The average percent of households with limited access to healthy food was 6.4%. In adjusted models, every 5 [micro]g/[m.sup.3]) increase of P[M.sub.2.5] was associated with 65% higher ADRD cumulative incidence (IRR = 1.65; 95% CI: 1.30, 2.09), where P[M.sub.2.5] at or below 12 [micro]g/[m.sup.3] is considered healthy. Compared to large urban census tracts, rural and small urban tracts had 10% (IRR = 1.10; 95% CI: 1.00, 1.23) and 5% (IRR = 1.05; 95% CI: 0.96, 1.16) higher ADRD, respectively. For every percent increase of the county population with limited access to healthy food, ADRD was 2% higher (IRR= 1.02; 95% CI: 1.01, 1.04). Conclusions: Neighborhood environment features, such as higher air pollution levels, were associated with higher neighborhood ADRD incidence. The INLA-SPDE method could have broad applicability to data collected across disparate spatial scales. https://doi.org/10.1289/EHP13183, Introduction Alzheimer's disease (AD) impacts an estimated 5.7 million adults in the US (1) and is ranked, along with other forms of dementia, as the sixth leading cause of death. [...]

Abstract Background Radiotracer extravasations, caused largely by faulty tracer injections, can occur in up to 23% of 18F-fluorodeoxyglucose (FDG) PET/CT scans and negatively impact radiological review and tracer quantification. Conventional radiological assessment of extravasation severity on PET has limited performance (e.g., extravasations frequently resolve before scanning) and practical drawbacks. In this study, we develop a new topical detector-based FDG extravasation severity classifier, calibrated from semi-quantitative PET measurements, and assess its performance on human subjects. Methods A retrospective study examined patients whose FDG injections had been monitored as part of their standard workup for PET/CT imaging. Topical uncollimated gamma ray detectors were applied proximal to the injection site and on the same location on the opposing arm, and readings were acquired continuously during radiotracer uptake. Patients were imaged with their arms in the PET field of view and total extravasation activity quantified from static PET images through a volume of interest approach. The image-derived activities were considered ground truth and used to calibrate and assess quantification of topical detector readings extrapolated to the start of PET imaging. The classifier utilizes the calibrated detector readings to produce four extravasation severity classes: none, minor, moderate, and severe. In a blinded study, a radiologist qualitatively labeled PET images for extravasation severity using the same classifications. The radiologist’s interpretations and topical detector classifications were compared to the ground truth PET results. Results Linear regression of log-transformed image-derived versus topical detector tracer extravasation activity estimates showed a strong correlation (R 2 = 0.75). A total of 24 subject scans were cross-validated with the quantitatively based classifier through a leave-one-out methodology. For binary classification (none vs. extravasated), the topical detector classifier had the highest overall diagnostic performance for identifying extravasations. Specificity, sensitivity, accuracy, and positive predictive value were 100.0%, 80.0%, 95.8%, and 100.0%, respectively, for the topical detector classifier and 31.6%, 100.0%, 45.8%, and 27.8%, respectively, for the radiological analysis. The topical detector classifier, with an optimal detection threshold, produced a significantly higher Matthews correlation coefficient (MCC) than the radiological analysis (0.87 vs. 0.30). Conclusions The topical detector binary classifier, calibrated using quantitative static PET measurements, significantly improves extravasation detection compared to qualitative image analysis.

As the second leading cause of death among Americans aged 10 to 34, suicide is a serious public health concern. One potential predictor of suicidality is dating violence (DV) victimization, such as any physical, psychological, or sexual abuse by a current or former intimate partner. However, little longitudinal data exists on the relationship between suicidal ideation and DV. To address this gap in knowledge, we leverage data from two years of our longitudinal study Dating It Safe. Specifically, we examine whether physical and psychological DV victimization is associated with subsequent suicidal ideation in our ethnically diverse sample of young adults (n = 678; mean age = 25 at Wave 9; 63.6% female). While physical DV victimization was not linked to suicidal ideation over time, psychological DV victimization was (χ2 = 7.28, p = 0.007 for females; χ2 = 4.87, p = 0.027 for males). That psychological abuse was potentially as or more impactful than physical violence is consistent with the broader literature on the deleterious impacts of psychological violence, as well as the limited longitudinal literature looking at DV and suicidality specifically. These findings reinforce the notion that psychological abuse is as consequential as physical violence in the long-term, has unique impacts on mental health, and points to the need for both suicide and violence intervention programs to address this form of dating violence victimization.

Migratory waterfowl are a harvested resource shared among multiple European countries, exposing them to potential overexploitation. Management of take is challenging since the life cycle of migratory waterfowl consists of several stages distributed among several locations, with possible spatio-temporal overlap among populations with differing population trends. Successful harvest management in such situations requires knowledge about the connections between breeding and non-breeding locations, and where birds are harvested. Breeding populations of Eurasian wigeon (Mareca penelope) are declining in Finland, underlining the need for more effective harvest management. Relative proportions and temporal distribution of local breeding birds and migrants from a larger Russian breeding population within the Finnish hunting bag has been unknown to date. We studied spatio-temporal origins of Finnish harvested wigeon by measuring stable-hydrogen (δ2H) isotope values from legally harvested birds. We modelled the changes in δ2H values of the feather samples within the hunting season using Gaussian processes and found that the origin of harvested wigeon in Finland changed during the hunting season and differed by age and sex. In juveniles and adult females but not in adult males, origin of harvested birds shifted from local and possibly western Russian birds to more long-distance migratory birds during the harvest season. These patterns likely reflected sex- and age-specific differences in migratory behaviour of Eurasian wigeon in the East Atlantic flyway, which can be used to guide future management and conservation of this species through the implementation of spatio-temporal harvest regulation. [ABSTRACT FROM AUTHOR]

Background: During the 2010 Deepwater Horizon (DWH) disaster, response and cleanup workers were potentially exposed to toxic volatile components of crude oil. However, to our knowledge, no study has examined exposure to individual oil spill-related chemicals in relation to cardiovascular outcomes among oil spill workers. Objectives: Our aim was to investigate the association of several spill-related chemicals [benzene, toluene, ethylbenzene, xylene, n-hexane (BTEX-H)] and total hydrocarbons (THC) with incident coronary heart disease (CHD) events among workers enrolled in a prospective cohort. Methods: Cumulative exposures to THC and BTEX-H across the cleanup period were estimated via a job-exposure matrix that linked air measurement data with self-reported DWH spill work histories. We ascertained CHD events following each worker's last day of cleanup work as the first self-reported physician-diagnosed myocardial infarction (MI) or a fatal CHD event. We estimated hazard ratios (HR) and 95% confidence intervals for the associations of exposure quintiles (Q) with risk of CHD. We applied inverse probability weights to account for bias due to confounding and loss to follow-up. We used quantile g-computation to assess the joint effect of the BTEX-H mixture. Results: Among 22,655 workers with no previous MI diagnoses, 509 experienced an incident CHD event through December 2019. Workers in higher quintiles of each exposure agent had increased CHD risks in comparison with the referent group (Q1) of that agent, with the strongest associations observed in Q5 (range of HR = 1.14-1.44). However, most associations were nonsignificant, and there was no evidence of exposure-response trends. We observed stronger associations among ever smokers, workers with Conclusions: Higher exposures to volatile components of crude oil were associated with modest increases in risk of CHD among oil spill workers, although we did not observe exposure-response trends. https://doi.org/10.1289/EHP11859, Introduction The 2010 Deepwater Horizon (DWH) disaster was the largest marine oil spill in U.S. history. (1) An estimated 4.9 million barrels of crude oil were discharged into the Gulf [...]

Abstract Background Peatlands are expected to experience sustained yet fluctuating higher temperatures due to climate change, leading to increased microbial activity and greenhouse gas emissions. Despite mounting evidence for viral contributions to these processes in peatlands underlain with permafrost, little is known about viruses in other peatlands. More generally, soil viral biogeography and its potential drivers are poorly understood at both local and global scales. Here, 87 metagenomes and five viral size-fraction metagenomes (viromes) from a boreal peatland in northern Minnesota (the SPRUCE whole-ecosystem warming experiment and surrounding bog) were analyzed for dsDNA viral community ecological patterns, and the recovered viral populations (vOTUs) were compared with our curated PIGEON database of 266,125 vOTUs from diverse ecosystems. Results Within the SPRUCE experiment, viral community composition was significantly correlated with peat depth, water content, and carbon chemistry, including CH4 and CO2 concentrations, but not with temperature during the first 2 years of warming treatments. Peat vOTUs with aquatic-like signatures (shared predicted protein content with marine and/or freshwater vOTUs) were significantly enriched in more waterlogged surface peat depths. Predicted host ranges for SPRUCE vOTUs were relatively narrow, generally within a single bacterial genus. Of the 4326 SPRUCE vOTUs, 164 were previously detected in other soils, mostly peatlands. None of the previously identified 202,371 marine and freshwater vOTUs in our PIGEON database were detected in SPRUCE peat, but 0.4% of 80,714 viral clusters (VCs, grouped by predicted protein content) were shared between soil and aquatic environments. On a per-sample basis, vOTU recovery was 32 times higher from viromes compared with total metagenomes. Conclusions Results suggest strong viral “species” boundaries between terrestrial and aquatic ecosystems and to some extent between peat and other soils, with differences less pronounced at higher taxonomic levels. The significant enrichment of aquatic-like vOTUs in more waterlogged peat suggests that viruses may also exhibit niche partitioning on more local scales. These patterns are presumably driven in part by host ecology, consistent with the predicted narrow host ranges. Although more samples and increased sequencing depth improved vOTU recovery from total metagenomes, the substantially higher per-sample vOTU recovery after viral particle enrichment highlights the utility of soil viromics. Video abstract The importance of Minnesota peat viromes in revealing terrestrial and aquatic niche partitioning for viral populations

Multiple sclerosis (MS) is a chronic inflammatory, demyelinating, and neurodegenerative disease of the central nervous system (CNS). In people with MS, impaired remyelination and axonal loss lead to debilitating long-term neurologic deficits. Current MS disease-modifying drugs mainly target peripheral immune cells and have demonstrated little efficacy for neuroprotection or promoting repair. To elucidate the pathological mechanisms and test therapeutic interventions, multiple animal models have been developed to recapitulate specific aspects of MS pathology, particularly the acute inflammatory stage. However, there are few animal models that facilitate the study of remyelination in the presence of inflammation, and none fully replicate the biology of chronic demyelination in MS. In this review, we describe the animal models that have provided insight into the mechanisms underlying demyelination, myelin repair, and potential therapeutic targets for remyelination. We highlight the limitations of studying remyelination in toxin-based demyelination models and discuss the combinatorial models that recapitulate the inflammatory microenvironment, which is now recognized to be a major inhibitor of remyelination mechanisms. These models may be useful in identifying novel therapeutics that promote CNS remyelination in inflammatory diseases such as MS.